Abstract
This study evaluated whether wavelet functions (Bior1.3, Bior2.4, Db4, Db8, Haar, Sym4, and Sym8) and decomposition levels (Levels 3–8) can estimate soil properties. The analysis is based on the discrete wavelet transform with partial least-squares (DWT–PLS) method, incorporated into a visible and near-infrared reflectance analysis. The improved DWT–PLS method (called DWT–Stepwise-PLS) enhances the accuracy of the quantitative analysis model with DWT–PLS. The cation exchange capacity (CEC) was best estimated by the DWT–PLS model using the Haar wavelet function. This model yielded the highest coefficient of determination (Rv2 = 0.787, p < 0.001), with the highest relative percentage deviation (RPD = 2.047) and lowest root mean square error (RMSE = 4.16) for the validation data set of the CEC. The RPD of the SOM predictions by DWT–PLS using the Bior1.3 wavelet function was maximized at 1.441 (Rv2 = 0.642, RMSE = 5.96), highlighting the poor overall predictive ability of soil organic matter (SOM) by DWT–PLS. Furthermore, the best performing decomposition levels of the wavelet function were distributed in the fifth, sixth, and seventh levels. For various wavelet functions and decomposition levels, the DWT–Stepwise-PLS method more accurately predicted the quantified soil properties than the DWT–PLS model. DWT–Stepwise-PLS using the Haar wavelet function remained the best choice for quantifying the CEC (Rv2 = 0.92, p < 0.001, RMSE = 4.91, and RPD = 3.57), but the SOM was better predicted by DWT–Stepwise-PLS using the Bior2.4 wavelet function (Rv2 = 0.8, RMSE = 5.34, and RPD = 2.24) instead of the Bior1.3 wavelet function. However, the performance of the DWT–Stepwise-PLS method tended to degrade at high and low decomposition levels of the DWT. These degradations were attributed to a lack of sufficient information and noise, respectively.
Highlights
Soil quality mainly depends on the chemical and physical properties of the soil, which are estimated by the cumulative effects of natural factors involved in its formation, including climate, topography, parent material, biological activity, and time [1]
This study compared the appropriateness of inserting different wavelet functions with different decomposition levels in the discrete wavelet transform with partial least-squares (DWT–partial least-squares regression (PLS)) method into the conventional visible and near infrared reflectance analysis method for estimating soil properties
The reliability and accuracy of the soil properties estimated by discrete wavelet transform-based visible and near infrared reflectance analysis was enhanced by an improved partial least-squares method called Stepwise-PLS
Summary
Soil quality mainly depends on the chemical and physical properties of the soil, which are estimated by the cumulative effects of natural factors involved in its formation, including climate, topography, parent material, biological activity, and time [1]. The development of precision agriculture requires a quantitative analysis technology that can accurately and quickly elucidate the physicochemical properties of soils over a large area. The visible and near infrared reflectance analysis (Vis-NIRA) technique has emerged as a possible enhancer or replacer of traditional soil testing methods. Using the Vis-NIRA technique, many researchers have related soil properties to spectroscopic soil reflectance data [4,5,6,7,8,9,10,11]. Because the extremely large volume of hyper-spectral data and visible and near-infrared hyper-spectra are difficult to interpret directly, since they contain overlapping weak overtones and combinations of fundamental vibrational bands, identifying the critical spectral features that estimate the soil properties is a difficult task
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